Perceiving Norm Change Through Intuitive Statistics

Abstract: People often infer social norms from behavioral regularity, yet it is unclear how they perceive norm change. We propose that people rely on intuitive statistics to track norms. Across six experiments, we tested which central tendency statistics guide norm inferences and how changes in these statistics influence perceived norm change. Experiments 1a–1e show that people treat the mode as more informative than the mean or median in inferring what the norm is, especially when modes are salient and under memory constraints. Experiment 2 characterizes norm change across four patterns: norm drift, norm emergence, norm erosion, and norm shift. Results indicate that perceived norm change is most sensitive to the emergence of a new modal behavior, particularly when the previous mode weakens, whereas mean shifts or mode erosion alone have minimal effect. These findings suggest that people adopt a frequency-based reasoning of norms, perceiving social change as discrete rather than gradual.

Keywords: social norms perception, social norm change, frequency distribution, statistical intuition